Advances in digital technology have resulted in the rapid growth of electronic media data such as still images, audio, video, graphics, and the like. For the purposes of this invention, media data includes any type of media or multimedia data including but not limited to still images, audio, video, graphics and the like. Because of this growth, there has been an increasing demand for methods and systems that enable a user to easily catalog, index and access the huge amounts of media data. However, unlike textual data, media data cannot be easily organized and searched using phrases, authors, and other traditional search terms. Techniques have been developed to address this problem with respect to still images.
One semi-automated grouping and retrieval technique for still images developed by IBM Corporation is based on image content and involves a “query by content (QBIC™)” paradigm. The QBIC™ technique relies upon classifying an image according to a small number of pre-defined fixed image features, such as distribution of color across an image, shapes in an image, and textures in an image. Once these attributes are measured for each image, a sequence of numeric values is generated for that image. When searching the QBIC™ database, queries are made by providing an example of an image similar to that which the user desires, and then setting a weight for each characteristic that the user believes accurately reflects the presence of each attribute in the desired image as compared to that in the test image. To retrieve the image, the system compares the vector for the test image, modified by weights provided by the user, to the vector for each of the images in the database.
Another technique for automating image retrieval includes mathematical techniques for looking for similarities in images. Yet another technique includes using the distribution of colors in an image to create a histogram of frequency of occurrences across a query image. The histogrammed distribution is measured for each image in the database, with an abstract-type distance measure used to compare the histogrammed results between each database image and the query image.
The foregoing techniques suffer from the disadvantage of only addressing retrieval of still images. In addition, these techniques require significant user input and are time intensive.
Therefore, there is a need for a method and apparatus for cataloging media objects in a database by forming an index to a collection of media objects. There is a need for a method and apparatus that provides organization of media objects using contextual information from which inferences may be drawn based upon known media objects, categories, indexes and searches. Further, a method and apparatus for searching and retrieving media objects from a database are needed. Moreover, there is a need for a method and apparatus for clustering media objects whereby media objects are automatically placed in a collection.